Papers to Appear in Subsequent Issues

Exact recovery in the Ising blockmodel Quentin Berthet, Philippe Rigollet, and Piyush Srivastava
Maximuim likelihood estimation in Gaussian models under total positivity Steffen Lilholt Lauritzen, Caroline Uhler, and Piotr Zwiernik
Testing in High-Dimensional Spiked Models Iain M Johnstone and Alexei Onatski
Bayesian fractional posteriors Anirban Bhattacharya, Debdeep Pati, and Yun Yang
Distributed Estimation of Principal Eigenspaces Jianqing Fan, Dong Wang, Kaizheng Wang, and Ziwei Zhu
Maximum likelihood estimation in transformed linear regression with non-normal errors Xingwei Tong, Fuqing Gao, Kani Chen, Dingjiao Cai, and Jianguo Sun
Hypothesis Testing for Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates Sivaraman Balakrishnan and Larry Wasserman
The BLUE in regression models with correlated errors Holger Dette, Andrey Pepelyshev, and Anatoly Zhigljavsky
Adaptive-to-model checking for regressions with diverging number of predictors Falong Tan and Lixing Zhu
Nonparametric Screening under Conditional Strictly Convex Loss for Ultrahigh Dimensional Sparse Data Xu Han
Local stationarity and time-inhomogeneous Markov chains Lionel Truquet
High-dimensional change-point detection with sparse alternatives Farida Enikeeva and Zaid Harchaoui
Perturbation Bootstrap in Adaptive Lasso Debraj Das, Karl Gregory, and Soumendra Nath Lahiri
Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions Guillaume Lecue, Pierre Alquier, and Vincent Cottet
Cross validation for locally stationary processes Stefan Richter and Rainer Dahlhaus
Generalized Cluster Trees and Singular Measures Yen-Chi Chen
Spectral Method and Regularized MLE are Both Optimal for Top-K Ranking Yuxin Chen, Jianqing Fan, Cong Ma, and Kaizheng Wang
Negative association, ordering and convergence of resampling methods Mathieu Gerber, Nicolas Chopin, and Nick Whiteley
On deep learning as a remedy for the curse of dimensionality in nonparametric regression Benedikt Bauer and Michael Kohler
Convergence rates of least squares regression estimators with heavy tailed errors Qiyang Han and Jon A. Wellner
Convergence complexity analysis of Albert and Chib’s algorithm for Bayesian probit regression Qian Qin and James P. Hobert
On Testing Conditional Qualitative Treatment Effects Chengchun Shi, Wenbin Lu, and Rui Song
Dynamic network models and graphon estimation Marianna Pensky
The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics Joshua Cape, Minh Tang, and Carey E. Priebe
Isotonic regression in general dimensions Qiyang Han, Tengyao Wang, Sabyasachi Chatterjee, and Richard John Samworth
Property Testing in High Dimensional Ising Models Matey Neykov and Han Liu
A knockoff filter for high-dimensional selective inference Rina Foygel Barber and Emmanuel J Candes
Semi-supervised Inference: General Theory and Estimation of Means Anru Zhang, Lawrence D. Brown, and T. Tony Cai
Penalized Estimation in Additive Regression with High-Dimensional Data Zhiqiang Tan and Cun-Hui Zhang
Semiparametrically Optimal Hybrid Rank Tests for Unit Roots Bo Zhou, Ramon van den Akker, and Bas Werker
Sorted Concave Penalized Regression Long Feng and Cun-Hui Zhang
The middle-scale asymptotics of Wishart matrices Didier Chételat and Martin T. Wells
Linear hypothesis testing for high dimensional generalized linear models Chengchun Shi, Rui Song, Zhao Chen, and Runze Li
An Operator Theoretic Approach to Nonparametric Mixture Models Robert Anton Vandermeulen and Clayton Scott
Phase transition in the spiked random tensor with Rademacher prior Wei-Kuo Chen
Distance multivariance: New dependence measures for random vectors Björn Böttcher, Martin Keller-Ressel, and Rene L. Schilling
A Unified Treatment of Multiple Testing with Prior Knowledge using the p-filter Aaditya K. Ramdas, Rina F. Barber, Martin J. Wainwright, and Michael I. Jordan
Exact Lower Bounds for the Agnostic Probably-Approximately-Correct (PAC) Machine Learning Model Iosif Pinelis and Aryeh Kontorovich
Eigenvalue distributions of variance components estimators in high-dimensional random effects models Zhou Fan and Iain Johnstone
Global Test Statistics for High Dimensional  Correlation Matrices S. R. Zheng, Guanghui Cheng, Jianhua Guo, and Hongtu Zhu
Projected Spline Estimation of the Nonparametric Function in High-dimensional Partially Linear Models for Massive Data Heng Lian, Kaifeng Zhao, and Shaogao Lv
Inference for the mode of a log-concave density Charles R. Doss and Jon A. Wellner
Testing for Independence of Large Dimensional Vectors Taras Bodnar, Holger Dette, and Nestor Parolya
Active Ranking from Pairwise Comparisons and When Parametric Assumptions Don’t Help Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran, and Martin J. Wainwright
Randomized incomplete U-statistics in high dimensions Xiaohui Chen and Kengo Kato
Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models Xin Bing and Marten Wegkamp
Statistical inference for autoregressive models under heteroscedasticity of unknown form Ke Zhu
On Partial-Sum Processes of ARMAX Residuals Steffen Grønneberg and Benjamin Holcblat
Quantile Regression Under Memory Constraint Xi Chen, Weidong Liu, and Yichen Zhang
Sampling and Estimation for (Sparse) Exchangeable Graphs Victor Veitch and Daniel Murphy Roy
Hypothesis Testing on Linear Structures of High Dimensional Covariance Matrix Shurong Zheng, Zhao Chen, Hengjian Cui, and Runze Li
On optimal designs for non-regular models Yi Lin, Ryan Martin, and Min Yang
A Smeary Central Limit Theorem for Manifolds with Application to High Dimensional Spheres Benjamin Eltzner and Stephan F. Huckemann
On testing for high-dimensional white noise Zeng Li, Jianfeng Yao, Clifford Lam,  and Qiwei Yao
Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors Kyoungjae Lee, Jaeyong Lee, and Lizhen Lin
Bootstrapping and Sample Splitting for High-Dimensional, Assumption-Free Inference Alessandro Rinaldo, Max G’Sell, Jing Lei, and Larry Wasserman
Joint convergence of sample autocovariance matrices when p/n → 0 with application Monika Bhattacharjee and Arup Bose
Tracy-Widom limit for Kendall’s tau Zhigang Bao
Intrinsic Riemannian Functional Data Analysis Zhenhua Lin and Fang Yao
Two-Step Semiparametric Empirical Likelihood Inference Francesco Bravo, Juan Carlos Escanciano, and Ingrid Van Keilegom
The Phase Transition for the Existence of the Maximum Likelihood Estimate in High-Dimensional Logistic Regression Emmanuel Jean Candes and Pragya Sur
Rerandomization in 2K Factorial Experiments Peng Ding, Xinran Li, and Donald Bruce Rubin
Sparse Sir: Optimal Rates and Adaptive Estimation Kai Tan, Lei Shi, and Zhou Yu
On Estimation of Isotonic Piecewise Constant Signals Chao Gao, Fang Han, and Cun-Hui Zhang
Robust Sparse Covariance Estimation by Thresholding Tyler’s M-Estimator John Goes, Gilad Lerman, and Boaz Nadler
Model-assisted variable clustering: minimax-optimal recovery and algorithms Florentina Bunea, Christophe Giraud, Martin Royer, Nicolas Verzelen, and Xi Luo
The New G-Formula for the Sequential Causal Effect and the Blip Effect of Treatment in Sequential Causal Inference Xiaoqin Wang and Li Yin
Envelope-Based Sparse Partial Least Squares Guangyu Zhu and Zhihua Su
Optimal Rates for Community Estimation in the Weighted Stochastic Block Model Min Xu, Varun Jog, and Po-Ling Loh
Limiting Laws for Divergent Spiked Eigenvalues and Largest Non-spiked Eigenvalue of Sample Covariance Matrices Tony Cai, Xiao Han, and Guangming Pan
Spatial Adaptation in Trend Filtering Adityanand Guntuboyina, Donovan Lieu, Sabyasachi Chatterjee, and Bodhisattva Sen
Spectral and matrix factorization methods for consistent community detection in multi-layer networks Subhadeep Paul and Yuguo Chen
Statistical Inference for Model Parameters in Stochastic Gradient Descent Xi Chen, Jason D. Lee, Xin T. Tong, and Yichen Zhang
Non-classical Berry-Esseen inequalities and accuracy of the bootstrap Mayya Zhilova
Bootstrap Confidence Regions based on M-Estimators under Nonstandard Conditions Stephen M.S. Lee and Puyudi Yang
Sparse high dimensional regression: Exact scalable algorithms and phase transitions Dimitris Bertsimas and Bart van Parys
Testing for Principal Component Directions under Weak Identifiability Davy Paindaveine, Julien Rémy, and Thomas Verdebout
Multidimensional multiscale scanning in Exponential Families: Limit theory and statistical consequences Claudia König, Axel Munk, and Frank Werner
Designs for estimating the treatment effect in networks with interference Ravi Jagadeesan, Natesh Pillai, and Alexander Volfovsky
Learning a Tree-Structured Ising Model in Order to Make Predictions Guy Bresler and Mina Karzand
The multi-armed bandit problem: an efficient non-parametric solution Hock Peng Chan
Concentration and Consistency Results for Canonical and Curved Exponential-Family Models of Random Graphs Michael Schweinberger and Jonathan Stewart
Change point analysis in non-stationary processes – a mass excess approach Holger Dette and Weichi Wu
The Numerical Bootstrap Han Hong and Jessie Li
On the optimality of sliced inverse regression in high dimensions Qian Lin, Jun S Liu, Dongming Huang, and Xinran Li
Consistent Selection of the Number of Change-Point Via Sample-Splitting Changliang Zou, Guanghui Wang, and Runze Li
Uniformly valid confidence intervals post-model-selection Francois Bachoc, David Preinerstorfer, and Lukas Steinberger
Efficient Estimation of Linear Functionals of Principal Components Vladimir Koltchinskii, Matthias Loeffler, and Richard Nickl
On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising Sujayam Saha and Adityanand Guntuboyina
Prediction error after model search Xiaoying Tian
Optimal Prediction in the Linearly Transformed Spiked Model Edgar Dobriban, William Leeb, and Amit Singer
Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior Eric Kolaczyk, Lizhen Lin, Steven Rosenberg, Jackson Walters, and Jie Xu
Markov equivalence of marginalized local independence graphs Søren Wengel Mogensen and Niels Richard Hansen
Joint estimation of parameters in Ising model Promit Ghosal and Sumit Mukherjee
Asymptotic genealogies of interacting particle systems with an application to sequential Monte Carlo Jere Koskela, Paul Jenkins, Adam Johansen, and Dario Spano
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data Zhiqiang Tan
Hurst Function Estimation Tailen Hsing and Jinqi Shen
Detection limits in the spiked Wigner model Ahmed El Alaoui, Florent Krzakala, and Michael Jordan
α-Variational Inference with Statistical Guarantees Yun Yang, Debdeep Pati, and Anirban Bhattacharya
Robust machine learning by median-of-means: theory and practice Guillaume Lecué and Matthieu Lerasle
Almost Sure Uniqueness of a Global Minimum Without Convexity Gregory Cox
Consistent Maximum Likelihood Estimation Using Subsets with Applications to Multivariate Mixed Models Karl Oskar Ekvall and Galin L. Jones
Local Asymptotics for Stochastic Optimization: Optimality, Constraint Identification, and Dual Averaging John Duchi and Feng Ruan
Convergence of eigenvector empirical spectral distribution of sample covariance matrices Jun Yin, Haokai Xi, Fan Yang
Additive Models with Trend Filtering Veeranjaneyulu Sadhanala, and Ryan Joseph Tibshirani
D-optimal Designs for Multinomial Logistic Models Xianwei Bu, Dibyen Majumdar, and Jie Yang
A unified study of nonparametric inference for monotone functions Ted Westling and Marco Carone
Inference for Archimax copulas Simon Chatelain, Anne-Laure Fougères, and Johanna G. Neslehova
Admissible Bayes equivariant estimation of location vectors for spherically symmetric distributions with unknown scale Yuzo Maruyama and William E. Strawderman
Worst-case vs Average-case Design for Estimation from Partial Pairwise Comparisons Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, and Thomas A. Courtade
Non-asymptotic upper bounds for the reconstruction error of PCA Martin Wahl and Markus Reiß
Lasso Guarantees for β-Mixing Heavy Tailed Time Series Kam Chung Wong, Zifan Li, and Ambuj Tewari
High-frequency analysis of parabolic stochastic PDEs Carsten Chong
Functional data analysis in the Banach space of continuous functions Holger Dette, Kevin Kokot, and Alexander Aue
Mean Estimation with Sub-Gaussian Rates in Polynomial Time Samuel Hopkins
Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional and Multinomial Data Miles Lopes, Zhenhua Lin, and Hans-Georg Mueller
Empirical Bayes oracle uncertainty quantification for regression Eduard Belitser and Subhashis Ghosal
GRID: A variable selection and structure discovery method for high dimensional nonparametric regression Soumendra N Lahiri
Post Hoc Confidence Bounds on False Positives Using Reference Families Gilles Blanchard, Pierre Neuvial, and Etienne Roquain
Distribution and Correlation Free Two-sample Test of High-dimensional Means Kaijie Xue and Fang Yao
Just Interpolate: Kernel “Ridgeless” Regression Can Generalize Tengyuan Liang and Alexander Rakhlin
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and  Markov Chains Alain Durmus, Aymeric Dieuleveut, and Francis Bach
Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions Richard Nickl and Kolyan Ray
Robust inference with knockoffs Rina Foygel Barber, Emmanuel J Candes, and Richard J Samworth